A method and system for abnormal monitoring and analysis based on water ecological environment

Abstract

The present invention is applicable to the field of ecological environment monitoring technology, and discloses a method and system for monitoring and analyzing abnormalities based on water ecological environment, the method comprising: real-time collection of ecological environment data corresponding to network nodes where sensors are deployed, and sending the ecological environment data to a cloud server; using Fanotify technology to track the path of the ecological environment data through the network node transmission process, and determining the traceability mark of the ecological environment data; analyzing the ecological environment data to generate water treatment chain data with three-layer association relationships; using a multi-path exploration algorithm to reversely trace the water treatment chain data to find the source of the abnormality. The solution of the present invention uses a multi-path exploration algorithm based on water treatment chain data to perform abnormality screening, which is more timely and accurate than global abnormality screening, and consumes less resources.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment monitoring, and in particular to an abnormal monitoring and analysis method and system based on water ecological environment. Background Art

[0002] With the rapid popularization of the Internet of Things, a large amount of ecological data with strong real-time, unstructured and diverse sources has been derived. At the same time, there are many factors that affect the ecological environment quality of a city or a region, such as air, surface water, groundwater, soil, noise, industry, life pollution, etc., and each factor can be subdivided into more items. The industries and fields involved in each factor are also very complex, resulting in a large amount of work and high difficulty in measuring ecological environment quality. The current measurement of ecological environment quality is more common through manual statistics of historical static data, and then the environmental quality is evaluated according to relevant rules. This method, on the one hand, relies on manual evaluation of ecological environment quality, which is inefficient. On the other hand, it relies on historical static data, cannot timely associate new environmental measurement factors and real-time environmental data, and cannot dynamically adjust environmental assessment according to the latest changes in the situation. The timeliness and accuracy of the evaluation data need to be improved.

[0003] Under the requirements of big data, how various environmental protection departments can unify the data and conduct comprehensive assessments and abnormal investigations on the ecological environment of the environmental protection areas they are responsible for has become an urgent problem that needs to be solved. Summary of the invention

[0004] To solve the above problems, the present invention provides an abnormal monitoring and analysis method and system based on water ecological environment, which uses a multi-path exploration algorithm based on water treatment chain data to perform abnormality investigation, which is more timely, accurate and consumes less resources than global abnormality investigation.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for monitoring and analyzing abnormalities based on aquatic ecological environment, comprising:

[0007] Collect the ecological environment data corresponding to the network nodes where sensors are deployed in real time, and send the ecological environment data to the cloud server;

[0008] Use Fanotify technology to track the path of ecological environment data transmission through network nodes and determine the traceability mark of ecological environment data;

[0009] Analyze ecological environment data to generate water treatment chain data with three-layer correlation relationships;

[0010] A multi-path exploration algorithm is used to reversely trace the water treatment chain data to find the source of the anomaly.

[0011] Ecological and environmental data include: network nodes, temperature and humidity, water quality, soil conditions, temperature, rainfall, and biological population size.

[0012] Analyze the ecological environment data to generate water treatment chain data with three layers of correlation, including:

[0013] According to the three indicators of network node IP, temperature and humidity, and water quality, the traceability data set related to the surrounding ecological environment is extracted from the ecological environment data;

[0014] Obtain traceability marks corresponding to ecological and environmental data;

[0015] Cluster the provenance dataset into several sub-categories;

[0016] According to the abnormal threshold of the reference indicator corresponding to the sub-classification, the traceability data is marked as abnormal;

[0017] Based on network node IP, temperature and humidity, water quality, sub-classification, traceability mark, and abnormal mark, water treatment chain data with three-layer association relationships is generated.

[0018] The traceability dataset is clustered into several subcategories, including water resource classification, land resource classification, biological resource classification, and climate resource classification.

[0019] In a second aspect, the present invention further provides an abnormal monitoring and analysis system based on aquatic ecological environment, comprising:

[0020] A collection module, which is used to collect the ecological environment data corresponding to the network nodes where sensors are deployed in real time, and send the ecological environment data to the cloud server;

[0021] The tagging module is used to use Fanotify technology to track the path of ecological environment data transmission through network nodes and determine the traceability tag of ecological environment data;

[0022] The analysis module is used to analyze the ecological environment data and generate water treatment chain data with three-layer association relationships;

[0023] The traceability module is used to reversely trace the water treatment chain data using a multi-path exploration algorithm to find the source of the anomaly.

[0024] Ecological and environmental data include: network node IP, temperature and humidity, water quality, soil conditions, temperature, rainfall, and biological population size.

[0025] Analyze the ecological environment data to generate water treatment chain data with three layers of correlation, including:

[0026] According to the three indicators of network node IP, temperature and humidity, and water quality, the traceability data set related to the surrounding ecological environment is extracted from the ecological environment data;

[0027] Obtain traceability marks corresponding to ecological and environmental data;

[0028] Cluster the provenance dataset into several sub-categories;

[0029] According to the abnormal threshold of the reference indicator corresponding to the sub-classification, the traceability data is marked as abnormal;

[0030] Based on network node IP, temperature and humidity, water quality, sub-classification, traceability mark, and abnormal mark, water treatment chain data with three-layer association relationships is generated.

[0031] The traceability dataset is clustered into several subcategories, including water resource classification, land resource classification, biological resource classification, and climate resource classification.

[0032] In a third aspect, the present invention further provides an abnormal monitoring and analysis system based on a water ecological environment, comprising: at least one sensor unit is deployed at each pipeline intersection node in the water ecological environment, the sensor unit is connected to a remote cloud server network, the sensor unit and the cloud server serve as network nodes, and constitute a water treatment network corresponding to the water ecological environment, and the cloud server includes a sensing unit and a central processor;

[0033] The sensor unit is used to collect the ecological environment data corresponding to the network nodes in real time and send the ecological environment data to the cloud server;

[0034] It is also used to track the path of ecological environment data through network nodes using Fanotify technology and determine the traceability mark of ecological environment data;

[0035] A sensing unit, used to receive ecological environment data and send the ecological environment data to a central processor;

[0036] The central processing unit is used to analyze the ecological environment data and generate water treatment chain data with three-layer correlation relationships;

[0037] It is also used to reversely trace the water treatment chain data using a multi-path exploration algorithm to find the source of the anomaly.

[0038] The system also includes:

[0039] The storage unit is used to store the received ecological environment data or water treatment chain data.

[0040] The technical solution provided by the present invention has at least the following technical effects or advantages:

[0041] By constructing an abnormal monitoring and analysis method and system based on water ecological environment, the ecological environment data collected in real time by the sensor unit is sent to the server side, and the Fanotify technology is used to track the transmission process of the ecological environment data through the network nodes during sending, and the traceability mark of the ecological environment data is determined. According to the three indicators of network node IP, temperature and humidity, and water quality, the traceability data set is extracted from the ecological environment data, and the traceability data set is analyzed to generate water treatment chain data with three layers of association. Based on the processing chain data, a multi-path exploration algorithm is used for tracing the source to assist in troubleshooting the source of abnormal data. The technical solution of the present invention performs abnormal investigation based on water treatment chain data, which is more timely and accurate than global abnormal investigation, and consumes less resources.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 Schematic diagram of a flow chart of an abnormal monitoring and analysis method based on a water ecological environment in an embodiment of the present invention;

[0045] Figure 2 Schematic diagram of the structure of an abnormal monitoring and analysis system based on water ecological environment in an embodiment of the present invention;

[0046] Figure 3 It is a structural schematic diagram of another abnormal monitoring and analysis system based on water ecological environment in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Fanotify (File Notification) technology is a mechanism for file system monitoring, which has certain application potential in tracking network transmission paths. Fanotify technology can monitor and notify file system access events. When applied to tracking network transmission paths, it can obtain information about data transmission by monitoring file operations related to network transmission. By analyzing and integrating this monitoring information, the flow path of data in network transmission can be outlined. Multipath exploration algorithm is a type of algorithm strategy used to simultaneously find multiple possible paths in a complex search space.

[0049] The technical solution of the present invention is applicable to abnormal monitoring and analysis based on water ecological environment, and the water ecological environment includes reservoirs and water plants.

[0050] Figure 1 This is an abnormal monitoring and analysis method based on water ecological environment in an embodiment of the present invention. As shown in the figure, the method includes:

[0051] S101, collecting ecological environment data corresponding to network nodes where sensors are deployed in real time, and sending the ecological environment data to a cloud server;

[0052] In the actual water ecological environment, sensors are deployed at the intersection nodes of each pipeline in the water ecological environment. The sensors are connected to the remote cloud server network. The sensors and cloud servers serve as network nodes to form a water treatment network corresponding to the water ecological environment. The sensors collect the ecological environment data corresponding to the network nodes in real time and send the ecological environment data to the cloud server. The ecological environment data includes: network node IP, temperature and humidity, water quality, soil conditions, temperature, rainfall, biological population, etc.

[0053] S102, using Fanotify technology to track the path of ecological environment data transmission through network nodes, and determine the traceability mark of ecological environment data;

[0054] Sensors and cloud servers serve as network nodes of the water treatment network. Each node integrates the Linux kernel system. Fanotify is started during the operation of the Linux system. Fanotify technology is used to track the path of ecological environment data transmission through network nodes and determine the traceability mark of ecological environment data. The traceability mark is used to determine the location where the anomaly occurs during anomaly troubleshooting.

[0055] S103, analyzing the ecological environment data to generate water treatment chain data with three-layer association relationships;

[0056] In actual usage scenarios, the amount of ecological and environmental data collected in real time is very large, and the daily data volume may reach tens of tB (TBs). However, a large amount of data requires a large storage capacity of the server, which in turn increases the purchase cost of the equipment. At the same time, when analyzing and tracing the abnormalities of ecological and environmental data, the timeliness of searching in the massive data is poor, which tests the load and pressure of the server to process a large amount of data, and will seriously affect the efficiency and stability of ecological and environmental monitoring. In order to solve the above problems, the ecological and environmental data are analyzed to generate water treatment chain data with three-layer association relationships, including the following steps:

[0057] According to the three indicators of network node IP, temperature and humidity, and water quality, data related to the surrounding ecological environment are extracted from the ecological environment data as the traceability data set; the traceability data includes: network node IP, temperature and humidity, water quality, soil conditions, temperature, humidity, rainfall, biological population size, etc.

[0058] Obtain a traceability mark corresponding to the ecological environment data; the traceability mark refers to the traceability mark corresponding to the ecological data determined by using Fanotify technology to track the path of the ecological environment data transmission process through network nodes when the sensor collects the ecological environment data in real time.

[0059] The traceability dataset is clustered into several sub-categories, including water resource classification, land resource classification, biological resource classification, and climate resource classification;

[0060] According to the abnormal threshold of the reference indicator corresponding to the sub-classification, the traceability data is marked as abnormal. If the value corresponding to the traceability data is lower than the abnormal threshold, it is marked as normal. If the value corresponding to the traceability data is greater than or equal to the abnormal threshold, it is marked as abnormal.

[0061] Based on network node IP, temperature and humidity, water quality, sub-classification, traceability mark, and abnormal mark, water treatment chain data with three layers of association is generated. The first layer is network node IP, temperature and humidity, water quality, and sub-classification; the second layer is sub-classification; and the third layer is traceability mark and abnormal mark.

[0062] For example, water processing chain data, such as “IP#temperature and humidity#water quality#water resource classification#land resource classification#biological resource classification#climate resource classification#traceability mark#abnormal mark”.

[0063] When tracing the source and locating anomalies based on water treatment chain data, there is no need to search through massive logs, but quick location and tracing can be achieved with less data.

[0064] S104: Use a multi-path exploration algorithm to reversely trace the water treatment chain data to find the source of the anomaly.

[0065] Since the sensors are connected to the remote cloud server network in the water ecological environment, the sensors and cloud servers serve as network nodes to form a water treatment network corresponding to the water ecological environment. The sensors collect the ecological environment data corresponding to the network nodes in real time and send the ecological environment data to the cloud server. During the transmission of ecological environment data in the water treatment network, multiple transmission paths will be generated, that is, multiple network transmission paths from sensor A that collects ecological environment data to cloud server B that receives ecological environment data. When performing reverse tracing based on the traceability marks in the water treatment network and water treatment chain data, a multi-path exploration algorithm can be used to obtain all paths from cloud server B that receives ecological environment data to sensor A that collects ecological environment data, and the path containing the most data in the water treatment chain data during reverse tracing is used as the optimal return path. In the process of reverse tracing of water treatment chain data, the abnormally associated links are checked, which is more timely, accurate, and consumes less resources than global abnormality checking.

[0066] The reverse tracing path from the cloud server B that receives the ecological environment data to the sensor A that collects the ecological environment data is realized by using a multi-path exploration algorithm. Since the Fanotify technology is used to track the path of the ecological environment data transmission process through the network nodes, the traceability mark of the ecological environment data is determined. The traceability mark combined with the multi-path exploration algorithm can discover the path nodes hidden in the network.

[0067] Conventional Fanotify technology is used to check for possible hidden nodes, and the multi-path exploration algorithm is assisted to record or restore the execution status of the conditional branches corresponding to the network transmission path. However, the disadvantage is that since the execution status when each branch instruction is reached is not stored, the target program needs to be run multiple times to realize multi-path exploration. In the present invention, the traceability mark in the water treatment chain data is used for traceability, which overcomes the above disadvantages.

[0068] A multi-path exploration algorithm is used to trace back the water treatment chain data. After each execution, the newly obtained execution trace is analyzed to find new conditional branches for the next execution. If a branch of a conditional branch has not been executed, its condition will be reversed at runtime. All previously reversed branches will also be executed with this newly selected branch instruction. This is because the newly selected conditional branch can be executed only if the previously reversed branch has been executed.

[0069] Figure 2 This is an abnormal monitoring and analysis system based on water ecological environment in an embodiment of the present invention. As shown in the figure, the system includes:

[0070] A collection module, which is used to collect the ecological environment data corresponding to the network nodes where sensors are deployed in real time, and send the ecological environment data to the cloud server;

[0071] The tagging module is used to use Fanotify technology to track the path of ecological environment data transmission through network nodes and determine the traceability tag of ecological environment data;

[0072] The analysis module is used to analyze the ecological environment data and generate water treatment chain data with three-layer association relationships;

[0073] The traceability module is used to reversely trace the water treatment chain data using a multi-path exploration algorithm to find the source of the anomaly.

[0074] Figure 3 This is a structural schematic diagram of another abnormal monitoring and analysis system based on a water ecological environment in an embodiment of the present invention. As shown in the figure, the system includes: at least one sensor unit is deployed at each pipeline intersection node in the water ecological environment, the sensor unit is connected to a remote cloud server network, the sensor unit and the cloud server serve as network nodes, and constitute a water treatment network corresponding to the water ecological environment. The cloud server includes a sensing unit and a central processor;

[0075] The sensor unit is used to collect the ecological environment data corresponding to the network nodes in real time and send the ecological environment data to the cloud server;

[0076] It is also used to track the path of ecological environment data through network nodes using Fanotify technology and determine the traceability mark of ecological environment data;

[0077] A sensing unit, used to receive ecological environment data and send the ecological environment data to a central processor;

[0078] The central processing unit is used to analyze the ecological environment data and generate water treatment chain data with three-layer correlation relationships;

[0079] It is also used to reversely trace the water treatment chain data using a multi-path exploration algorithm to find the source of the anomaly.

[0080] The abnormal monitoring and analysis system based on the water ecological environment also includes: a storage unit for storing received ecological environment data or water treatment chain data;

[0081] The abnormal monitoring and analysis system based on the water ecological environment also includes: an execution unit for executing central processing unit commands.

[0082] The technical solution in the embodiments of the present invention has at least the following technical effects or advantages:

[0083] By constructing an abnormal monitoring and analysis method and system based on water ecological environment, the ecological environment data collected in real time by the sensor unit is sent to the server side, and the Fanotify technology is used to track the transmission process of the ecological environment data through the network nodes during sending, and the traceability mark of the ecological environment data is determined. According to the three indicators of network node IP, temperature and humidity, and water quality, the traceability data set is extracted from the ecological environment data, and the traceability data set is analyzed to generate water treatment chain data with three layers of association. Based on the processing chain data, a multi-path exploration algorithm is used for tracing the source to assist in troubleshooting the source of abnormal data. The technical solution of the present invention performs abnormal investigation based on water treatment chain data, which is more timely and accurate than global abnormal investigation, and consumes less resources.

[0084] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for abnormal monitoring and analysis based on water ecological environment, characterized in that: include: Collecting ecological environment data corresponding to network nodes where sensors are deployed in real time, and sending the ecological environment data to a cloud server; Adopting Fanotify technology to track the path of the ecological environment data through the network node transmission process and determine the traceability mark of the ecological environment data; Analyze the ecological environment data to generate water treatment chain data with three-layer association relationships; A multi-path exploration algorithm is used to reversely trace the water treatment chain data, and the path containing the most data in the water treatment chain data during reverse tracing is used as the optimal return path to find the source of the anomaly; The ecological environment data include: network nodes, temperature and humidity, water quality, soil conditions, temperature, rainfall, and biological population; The ecological environment data is analyzed to generate water treatment chain data with three-layer association relationships, including: Extract the traceability data set related to the surrounding ecological environment from the ecological environment data based on the three indicators of network node IP, temperature and humidity, and water quality; Obtaining a traceability mark corresponding to the ecological environment data; Cluster the provenance dataset into several sub-categories; According to the abnormal threshold of the reference indicator corresponding to the sub-classification, the traceability data is marked as abnormal; Generate water treatment chain data with three-layer association relationships based on network node IP, temperature and humidity, water quality, sub-classification, traceability mark, and abnormal mark; The traceability data set is clustered into several sub-categories, wherein the several sub-categories include: water resource classification, land resource classification, biological resource classification, and climate resource classification.

2. An abnormal monitoring and analysis system based on water ecological environment, characterized in that: include: A collection module, used to collect ecological environment data corresponding to network nodes where sensors are deployed in real time, and send the ecological environment data to a cloud server; A marking module, used to track the path of the ecological environment data through the network node transmission process using the Fanotify technology, and determine the traceability mark of the ecological environment data; An analysis module, used to analyze the ecological environment data and generate water treatment chain data with three-layer association relationships; A tracing module is used to use a multi-path exploration algorithm to reversely trace the water treatment chain data, and take the path containing the most data in the water treatment chain data during reverse tracing as the optimal return path to find the source of the anomaly; The ecological environment data include: network nodes, temperature and humidity, water quality, soil conditions, temperature, rainfall, and biological population; The ecological environment data is analyzed to generate water treatment chain data with three-layer association relationships, including: Extract the traceability data set related to the surrounding ecological environment from the ecological environment data based on the three indicators of network node IP, temperature and humidity, and water quality; Obtaining a traceability mark corresponding to the ecological environment data; Cluster the provenance dataset into several sub-categories; According to the abnormal threshold of the reference indicator corresponding to the sub-classification, the traceability data is marked as abnormal; Generate water treatment chain data with three-layer association relationships based on network node IP, temperature and humidity, water quality, sub-classification, traceability mark, and abnormal mark; The traceability data set is clustered into several sub-categories, wherein the several sub-categories include: water resource classification, land resource classification, biological resource classification, and climate resource classification.

3. An abnormal monitoring and analysis system based on water ecological environment, characterized in that: include: At least one sensor unit is deployed at each pipeline intersection node in the water ecological environment. The sensor unit is connected to a remote cloud server network. The sensor unit and the cloud server serve as network nodes to form a water treatment network corresponding to the water ecological environment. The cloud server includes a sensing unit and a central processor. The sensor unit is used to collect the ecological environment data corresponding to the network nodes in real time and send the ecological environment data to the cloud server; It is also used to use Fanotify technology to track the path of the ecological environment data through the network node transmission process and to mark the ecological environment data for traceability; A sensing unit, used to receive ecological environment data and send the ecological environment data to a central processor; A central processing unit, used for analyzing the ecological environment data to generate water treatment chain data with three-layer association relationships; It is also used to reversely trace the water treatment chain data using a multi-path exploration algorithm, taking the path containing the most data in the water treatment chain data during reverse tracing as the optimal return path to find the source of the anomaly; The ecological environment data include: network nodes, temperature and humidity, water quality, soil conditions, temperature, rainfall, and biological population; The ecological environment data is analyzed to generate water treatment chain data with three-layer association relationships, including: Extract the traceability data set related to the surrounding ecological environment from the ecological environment data based on the three indicators of network node IP, temperature and humidity, and water quality; Obtaining a traceability mark corresponding to the ecological environment data; Cluster the provenance dataset into several sub-categories; According to the abnormal threshold of the reference indicator corresponding to the sub-classification, the traceability data is marked as abnormal; Generate water treatment chain data with three-layer association relationships based on network node IP, temperature and humidity, water quality, sub-classification, traceability mark, and abnormal mark; The traceability data set is clustered into several sub-categories, wherein the several sub-categories include: water resource classification, land resource classification, biological resource classification, and climate resource classification.

4. The abnormal monitoring and analysis system based on water ecological environment according to claim 3 is characterized in that: The system further comprises: The storage unit is used to store the received ecological environment data or water treatment chain data.

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